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tresor4k

macalc

calculate_fabric_yardage

Calculate meters of fabric needed for a garment, including 10% for pattern matching. Specify garment type (shirt, dress, pants, skirt, jacket) and size (S to XL) to get the required yardage.

Instructions

Calculate fabric needed for a garment in meters (includes 10% for pattern matching). Returns: {meters_needed, note}. See list_bundles for related 'textile-mode' calculators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
garmentYes
sizeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoComputed result. Object whose fields depend on the tool (e.g. {tax, marginal_rate, brackets} for tax tools, {volume_l, gallons} for volume tools).
formulaNoHuman-readable formula or method used (e.g. "I=P·r·t", "Magnus formula").
sourceNoAuthoritative source for the rule or formula (e.g. "Article 197 CGI", "NF DTU 21").
reference_urlNoLink to a calcul2 page documenting the calculation in detail.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the 10% pattern matching and the return format. With no annotations, it provides basic behavioral context. However, it omits details like accuracy, rounding, or assumptions about the calculation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two succinct sentences that convey the core purpose and an important behavioral note. No unnecessary words or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of a sibling 'calculate_fabric_needed', the description lacks sufficient detail to distinguish them. It does not cover edge cases, error handling, or assumptions. The output schema is mentioned but not elaborated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not mention the parameters 'garment' and 'size' despite 0% schema coverage. Although the parameter names are self-explanatory, the description fails to add any meaning beyond the schema's enum values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool calculates fabric needed for a garment in meters, including a 10% margin for pattern matching. It specifies the return structure. However, it does not differentiate from the sibling tool 'calculate_fabric_needed', which might cause confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The mention of 'list_bundles' is indirect and does not provide clear usage conditions or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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